Many computer programs execute a sequence of phases during execution; during each phase, the program executes some well-defined task. Indeed, since the inception of computer programming, programs have been described in terms of flow charts, which show graphical representations of various program phases and transitions among them. A phase is a method or function performed by a computer program. A phase begins when the program calls a method or process and completes when the result of the method is obtained.
Some previous work has suggested methods wherein if a program execution engine could detect phase shifts, it would exploit the information to improve system performance. Relevant related work is discussed in “Design and Implementation of the Opportunistic Garbage Collector,” by Wilson and Moher, in Proceedings of the ACM Conference on Object-Oriented Programming Languages, Systems and Applications, 1989, which describes a system that triggers garbage collection based on a heuristic process that correlates program phases with stack height.
Several works describe methods whereby an online adaptive optimization system could reset profit data based on automatic phase shift detection. See “Online Feedback-Directed Optimization of Java,” Arnold, Hind and Ryder, in Proceedings of the ACM Conference on Object-Oriented Programming Languages, Systems, and Applications, 2002; “Dynamic Hot Data Stream Prefetching for General-Purpose Programs,” Chilimbi and Hirzel, in SIGPLAN 2002 Conference on Programming Language Design and Implementation, 2002; “Computing the Similarity of Profiling Data; Heuristics for Guiding Adaptive Compilation,” by Kistler and Franz, in Workshop on Profile and Feedback-Directed Compilation, 1998.
Some of the known art in phase detection techniques for running computer programs looks for patterns in a stream of sequential data, reminiscent to algorithms commonly used in signal processing applications. These techniques require much tuning for sensitivity in edge detection, and rely on numerous heuristic filters to clean up noisy data.
Instead, it would be highly desirable to provide a method that exploits information that can be gathered by the program execution engine, in order to report phase boundaries definitively, without complications of sensitivity tuning and filter heuristics.
The previous work, by Wilson and Moher (referenced above), uses heuristics to detect phases of computer programs that provide good opportunities for garbage collection. One method described tags user-interaction routines, and tries to schedule garbage collection during pauses between such routines. The Wilson, et al. paper also suggests taking the height of activation stacks into account. A similar idea, for scheduling garbage collection (GC) based on stack pops, was suggested in “A Real-Time Garbage Collector Based on the Lifetimes of Objects,” by Lieberman and Hewitt, Communications of the ACM, 26(6), June 1983.
These references suggest using stack activation height as an indication of phase behavior. However, a modified program execution engine can further provide information regarding the lifetime of individual activations. There is thus a need for a method to exploit additional information for accurate and simple online phase detection. Further, there is a need for a method that can be implemented with low runtime overhead and can detect nested phases.